Finding Salient Dates for Building Thematic Timelines Guidelines
Rémy Kessler, Xavier Tannier, Caroline Hagege, Véronique Moriceau, André Bittar
We present an approach for detecting salient
(important) dates in texts in order to automatically
build event timelines from a search
query (e.g. the name of an event or person,
etc.). This work was carried out on a corpus
of newswire texts in English provided by the
Agence France Presse (AFP). In order to extract
salient dates that warrant inclusion in an
event timeline, we first recognize and normalize
temporal expressions in texts and then use
a machine-learning approach to extract salient
dates that relate to a particular topic. We focused
only on extracting the dates and not the events to which they are related.
The 50th Annual Meeting of the Association for Computational Linguistics, Jeju, Republic of Korea, July 8-14 2012.